{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "bb512df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "eeaacb29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "011607ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moen, Mr. Sigurd Hansen</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>348123</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>F G73</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Staneff, Mr. Ivan</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349208</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moutal, Mr. Rahamin Haim</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374746</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dowdell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364516</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "..          ...       ...     ...   \n",
       "75           76         0       3   \n",
       "76           77         0       3   \n",
       "77           78         0       3   \n",
       "78           79         1       2   \n",
       "79           80         1       3   \n",
       "\n",
       "                                                 Name     Sex    Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.00      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.00      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.00      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.00      1   \n",
       "4                            Allen, Mr. William Henry    male  35.00      0   \n",
       "..                                                ...     ...    ...    ...   \n",
       "75                            Moen, Mr. Sigurd Hansen    male  25.00      0   \n",
       "76                                  Staneff, Mr. Ivan    male    NaN      0   \n",
       "77                           Moutal, Mr. Rahamin Haim    male    NaN      0   \n",
       "78                      Caldwell, Master. Alden Gates    male   0.83      0   \n",
       "79                           Dowdell, Miss. Elizabeth  female  30.00      0   \n",
       "\n",
       "    Parch            Ticket     Fare  Cabin Embarked  \n",
       "0       0         A/5 21171   7.2500    NaN        S  \n",
       "1       0          PC 17599  71.2833    C85        C  \n",
       "2       0  STON/O2. 3101282   7.9250    NaN        S  \n",
       "3       0            113803  53.1000   C123        S  \n",
       "4       0            373450   8.0500    NaN        S  \n",
       "..    ...               ...      ...    ...      ...  \n",
       "75      0            348123   7.6500  F G73        S  \n",
       "76      0            349208   7.8958    NaN        S  \n",
       "77      0            374746   8.0500    NaN        S  \n",
       "78      2            248738  29.0000    NaN        S  \n",
       "79      0            364516  12.4750    NaN        S  \n",
       "\n",
       "[80 rows x 12 columns]"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "0d26d064",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
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       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
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      ],
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "ecf5fe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "2fbae5d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "bc1001e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "10c288e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "2ae00c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "c19f9311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "60db1ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set() # setting seaborn default for plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "36cf95a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_chart(feature):\n",
    "    survived = train[train['Survived']==1][feature].value_counts()\n",
    "    dead = train[train['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived','Dead']\n",
    "    df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "f8580f9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "c2776124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "2854347c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "56337311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Parch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "fbe92dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "bed50e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "cbea0384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg?format=1500w\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "b033431d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_data = [train, test] # combining train and test dataset\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "ef59ae52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Mlle          2\n",
       "Major         2\n",
       "Col           2\n",
       "Countess      1\n",
       "Capt          1\n",
       "Ms            1\n",
       "Sir           1\n",
       "Lady          1\n",
       "Mme           1\n",
       "Don           1\n",
       "Jonkheer      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "4765793a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Ms          1\n",
       "Dr          1\n",
       "Dona        1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "85dd41a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "a8544a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "23ddefcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Kelly, Mr. James</td>\n",
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       "      <td>0</td>\n",
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       "      <td>female</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>62.0</td>\n",
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       "      <td>female</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q      0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S      2  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q      0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S      0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S      2  "
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "c208318f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "386f7a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete unnecessary feature from dataset\n",
    "train.drop('Name', axis=1, inplace=True)\n",
    "test.drop('Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "f79f3714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "baa4561b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "1c513b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "01b21a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>231919</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244367</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0             1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  38.0      1      0          PC 17599   \n",
       "2             3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  35.0      1      0            113803   \n",
       "4             5         0       3    0  35.0      0      0            373450   \n",
       "..          ...       ...     ...  ...   ...    ...    ...               ...   \n",
       "95           96         0       3    0   NaN      0      0            374910   \n",
       "96           97         0       1    0  71.0      0      0          PC 17754   \n",
       "97           98         1       1    0  23.0      0      1          PC 17759   \n",
       "98           99         1       2    1  34.0      0      1            231919   \n",
       "99          100         0       2    0  34.0      1      0            244367   \n",
       "\n",
       "       Fare    Cabin Embarked  Title  \n",
       "0    7.2500      NaN        S      0  \n",
       "1   71.2833      C85        C      2  \n",
       "2    7.9250      NaN        S      1  \n",
       "3   53.1000     C123        S      2  \n",
       "4    8.0500      NaN        S      0  \n",
       "..      ...      ...      ...    ...  \n",
       "95   8.0500      NaN        S      0  \n",
       "96  34.6542       A5        C      0  \n",
       "97  63.3583  D10 D12        C      0  \n",
       "98  23.0000      NaN        S      2  \n",
       "99  26.0000      NaN        S      0  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "e6bafeda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
    "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
    "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "be3f96e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30.0\n",
       "1      35.0\n",
       "2      21.0\n",
       "3      35.0\n",
       "4      30.0\n",
       "       ... \n",
       "886     9.0\n",
       "887    21.0\n",
       "888    21.0\n",
       "889    30.0\n",
       "890    30.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(30)\n",
    "train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "1beb1f1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "bb2fed9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "b7aa6009",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(20.0, 30.0)"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(20, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "4392c3f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(30.0, 40.0)"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(30, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "e3b66f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(40.0, 60.0)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(40, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "d6e4ca72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(60.0, 80.0)"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "5426f3cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        204 non-null    object \n",
      " 10  Embarked     889 non-null    object \n",
      " 11  Title        891 non-null    int64  \n",
      "dtypes: float64(2), int64(7), object(3)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "972c6932",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Ticket       418 non-null    object \n",
      " 7   Fare         417 non-null    float64\n",
      " 8   Cabin        91 non-null     object \n",
      " 9   Embarked     418 non-null    object \n",
      " 10  Title        418 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(3)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "cb0f0b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2\n",
    "    \n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3\n",
    "    \n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "150ad17b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "a5ba0c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "4d467726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACA/9PiS9AJdrtddnug1TEAAAAAWIzTAwAAAACM0uQSVFRUpF69emnZsmW1Y4WFhcrMzFRycrL69u2rvLy8Oo/xeDzKzc1VWlqaevbsqREjRqi4uLjp6QEAAACgkZpUgo4dO6ZHHnlER48erR0rLy/X8OHD1blzZ+Xn52vUqFGaOXOm8vPza/eZPXu2Fi9erEmTJmnJkiWy2WzKysqSy+U6/SMBAAAAgAZoUgl68cUX1apVqzpjS5cuVWBgoMaPH6+EhAQNHjxYw4YN05w5cyRJLpdL8+bN06hRo5Senq7ExETNmDFD+/bt05o1a07/SAAAAACgARpdgtatW6clS5bo2WefrTO+fv16paSkKCDg57UWUlNTVVRUpLKyMm3dulVHjhxRampq7faIiAh1795d69atO41DAAAAAICGa9TqcE6nUzk5ORo3bpzi4+PrbCspKVG3bt3qjMXGxkqS9uzZo5KSEkmq97jY2Fjt3bu30cH/VUAAazyYyuGw1/kTgHmYBwBe//gZr4Vf1qgSNH78eCUnJ+vaa6+tt62qqkqBgXWXoA4KCpIkVVdXq7KyUpJOus+hQ4caFfpf2e02RUW1+uUd0aJFRIRYHQGAxZgHAIC5sCEaXILeeecdrV+/Xu++++5JtwcHB9db4KC6ulqSFBoaquDgYEnH3xt04uMT+4SEnN43yuPxyuk8+ss7okVyOOyKiAiR01kpt9tjdRwAFmAeAH7+OQBMngsjIkIadCaswSUoPz9fZWVl6tu3b53xJ598Unl5eWrfvr1KS0vrbDvxebt27VRTU1M71qlTpzr7JCYmNjTGKdXUmPmNxs/cbg+vA8BwzAMAwFzYEA0uQdOmTVNVVVWdsauvvlrZ2dkaMGCAVq5cqcWLF8vtdsvhcEiSCgoK1KVLF8XExCg8PFxhYWFau3ZtbQlyOp3asmWLMjMzz+AhAQAAAMCpNbgEtWvX7qTjMTEx6tChgwYPHqy5c+dq7NixuuOOO/Tll19qwYIFmjBhgqTj7wXKzMzUtGnTFB0drQ4dOmjq1KmKi4tT//79z8zRAAAAAMAvaNTCCP9OTEyM5s6dq8mTJysjI0Nt27ZVTk6OMjIyavfJzs5WTU2Nxo0bp6qqKqWkpCgvL6/eYgkAAAAAcLbYvF6v1+oQp8vt9ujAgSNWx4BFAgLsiopqpfLyI1z/ChiKeQD4+efgx7xH5CopsjoOLBAY10Udb59m9FwYHd2qQQsjsIg4AAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCgBVgcAAADAmRMY09HqCLAI3/uGowQBAAC0EF6PR7E3PGB1DFjI6/FYHcEvUIIAAABaCJvdrtdXbVHpgaNWR4EFYqNDdeuA7lbH8AuUIAAAgBbkH1tLtXP3IatjwAIJHVpTghqIhREAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABglEaXoLKyMo0ePVqpqanq1auX7rzzTu3YsaN2e2FhoTIzM5WcnKy+ffsqLy+vzuM9Ho9yc3OVlpamnj17asSIESouLj79IwEAAACABmh0Cbr77ru1a9cuzZkzR2+//baCg4M1bNgwVVZWqry8XMOHD1fnzp2Vn5+vUaNGaebMmcrPz699/OzZs7V48WJNmjRJS5Yskc1mU1ZWllwu1xk9MAAAAAA4mYDG7FxeXq6OHTvq7rvv1gUXXCBJuueee3T99dfr22+/VUFBgQIDAzV+/HgFBAQoISFBxcXFmjNnjgYPHiyXy6V58+Zp9OjRSk9PlyTNmDFDaWlpWrNmjQYOHHjmjxAAAAAA/kmjzgRFRUVp+vTptQXop59+Ul5enuLi4tS1a1etX79eKSkpCgj4uVulpqaqqKhIZWVl2rp1q44cOaLU1NTa7REREerevbvWrVt3hg4JAAAAAE6tUWeC/tnjjz+upUuXKjAwUC+99JJCQ0NVUlKibt261dkvNjZWkrRnzx6VlJRIkuLj4+vts3fv3qZGAQAAAIAGa3IJuu2223TzzTfrzTff1L333qtFixapqqpKgYGBdfYLCgqSJFVXV6uyslKSTrrPoUOHmhpFkhQQwEJ3pnI47HX+BGAe5gGA1z9+xmvhlzW5BHXt2lWS9NRTT2nTpk164403FBwcXG+Bg+rqaklSaGiogoODJUkul6v24xP7hISENDWK7HaboqJaNfnxaBkiIpr+GgLQMjAPAABzYUM0qgSVlZWpoKBA11xzjRwOhyTJbrcrISFBpaWliouLU2lpaZ3HnPi8Xbt2qqmpqR3r1KlTnX0SExObfBAej1dO59EmPx7+zeGwKyIiRE5npdxuj9VxAFiAeQD4+ecAMHkujIgIadCZsEaVoNLSUj388MOKiYnRpZdeKkk6duyYtmzZon79+qlNmzZavHix3G53bUkqKChQly5dFBMTo/DwcIWFhWnt2rW1JcjpdGrLli3KzMxs7DHWUVNj5jcaP3O7PbwOAMMxDwAAc2FDNOqCwcTERF1++eWaMGGC1q9fr+3bt+vRRx+V0+nUsGHDNHjwYFVUVGjs2LHasWOHli1bpgULFuiuu+6SdPy9QJmZmZo2bZo++ugjbd26VQ8++KDi4uLUv3//s3KAAAAAAPDPGnUmyGaz6YUXXtDzzz+vBx54QIcPH9Yll1yihQsXqn379pKkuXPnavLkycrIyFDbtm2Vk5OjjIyM2q+RnZ2tmpoajRs3TlVVVUpJSVFeXl69xRIAAAAA4Gyweb1er9UhTpfb7dGBA0esjgGLBATYFRXVSuXlRzj1CxiKeQD4+efggekfa+fu01t1F/4poUNrvfBQX6PnwujoVg16TxDr5wEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADBKgNUBcPrsdpvsdpvVMSzjcNjr/Gkij8crj8drdQwAAAC/QAnyc3a7TZFRIXLYHVZHsVxERIjVESzj9rh1sLySIgQAANAAlCA/Z7fb5LA7lP/NKu0/esDqOLBA29BoDe4xQHa7jRIEAADQAJSgFsDj8WhwjwFWx4CFPB6P1REAAAD8BiWoBbDb7Trwvwt17FCp1VFggXNaxyr6N0OsjgEAAOA3KEEtxNHvNspVUmR1DFggMK4LJQgAAKARGrWc1sGDB/XEE0/oiiuuUO/evfWHP/xB69evr91eWFiozMxMJScnq2/fvsrLy6vzeI/Ho9zcXKWlpalnz54aMWKEiouLz8yRAAAAAEADNKoEPfTQQ9q8ebOmT5+ut99+Wz169NDtt9+unTt3qry8XMOHD1fnzp2Vn5+vUaNGaebMmcrPz699/OzZs7V48WJNmjRJS5Yskc1mU1ZWllwu1xk/MAAAAAA4mQZfDldcXKzPP/9cb775pnr37i1JGjt2rD799FOtWLFCwcHBCgwM1Pjx4xUQEKCEhAQVFxdrzpw5Gjx4sFwul+bNm6fRo0crPT1dkjRjxgylpaVpzZo1Gjhw4Nk5QgAAAAD4Jw0+ExQVFaVXX31VF198ce2YzWaT1+vVoUOHtH79eqWkpCgg4OdelZqaqqKiIpWVlWnr1q06cuSIUlNTa7dHRESoe/fuWrdu3Rk6HAAAAAD49xpcgiIiIpSenq7AwMDasffee08//PCDLr/8cpWUlCguLq7OY2JjYyVJe/bsUUlJiSQpPj6+3j579+5t8gEAAAAAQGM0eXW4DRs2aMyYMbryyivVr18/TZkypU5BkqSgoCBJUnV1tSorKyXppPscOnSoqTFqBQQ06u1NLYbDYeZxoz5eCzDZidc/PwcwGa9/nMBr4Zc1qQR9+OGHeuSRR9SzZ09Nnz5dkhQcHFxvgYPq6mpJUmhoqIKDgyVJLper9uMT+4SEhDQp/Al2u01RUa1O62sA/i4i4vR+joCWgJ8DAGAubIhGl6A33nhDkydPVv/+/TVt2rTaMztxcXEqLa17s84Tn7dr1041NTW1Y506daqzT2JiYpMPQJI8Hq+czqOn9TX8lcNh54UOSZLTWSm322N1DMASJ+ZCfg5gMn4nwAkmz4URESENOhPWqBK0aNEiPfXUUxo6dKjGjBkju/3nJ0hJSdHixYvldrvlcDgkSQUFBerSpYtiYmIUHh6usLAwrV27trYEOZ1ObdmyRZmZmY2JcVI1NWZ+o4ET3G4PPwcwHj8HAMBc2BANvmCwqKhITz/9tPr376+77rpLZWVl2r9/v/bv36/Dhw9r8ODBqqio0NixY7Vjxw4tW7ZMCxYs0F133SXp+HuBMjMzNW3aNH300UfaunWrHnzwQcXFxal///5n7QABAAAA4J81+EzQ+++/r2PHjmnNmjVas2ZNnW0ZGRl65plnNHfuXE2ePFkZGRlq27atcnJylJGRUbtfdna2ampqNG7cOFVVVSklJUV5eXn1FksAAAAAgLOlwSVo5MiRGjly5L/dJykpSUuWLDnldofDodGjR2v06NENTwgAAAAAZxDr5wEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjNOpmqQCA5stut8lut1kdwxIn7g7ekLuEt2Qej1cej9fqGADQ7FGCAKAFsNttiowKkcPusDqKpSIiQqyOYCm3x62D5ZUUIQD4BZQgAGgB7HabHHaH8r9Zpf1HD1gdBxZoGxqtwT0GyG63UYIA4BdQggCghfB4PBrcY4DVMWAhj8djdQQA8AuUIABoIex2uw7870IdO1RqdRRY4JzWsYr+zRCrYwCAX6AEAUALcvS7jXKVFFkdAxYIjOtCCQKABjJ7GR0AAAAAxqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCjcLBUAWpDAmI5WR4BF+N4DQMNRggCghfB6PIq94QGrY8BCXo/H6ggA4BcoQQDQQtjsdr2+aotKDxy1OgosEBsdqlsHdLc6BgD4BUoQALQg/9haqp27D1kdAxZI6NCaEgQADcTCCAAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFECrA6AMyMwpqPVEWARvvcAAACNQwlqAbwej2JveMDqGLCQ1+OxOgIAAIDfoAS1ADa7Xa+v2qLSA0etjgILxEaH6tYB3a2OAQAA4DcoQS3EP7aWaufuQ1bHgAUSOrSmBAEAADQCCyMAAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYJTTKkGzZ8/W0KFD64wVFhYqMzNTycnJ6tu3r/Ly8ups93g8ys3NVVpamnr27KkRI0aouLj4dGIAAAAAQIM1uQTNnz9fubm5dcbKy8s1fPhwde7cWfn5+Ro1apRmzpyp/Pz82n1mz56txYsXa9KkSVqyZIlsNpuysrLkcrmafhQAAAAA0EABjX3Avn37NHbsWG3YsEFdunSps23p0qUKDAzU+PHjFRAQoISEBBUXF2vOnDkaPHiwXC6X5s2bp9GjRys9PV2SNGPGDKWlpWnNmjUaOHDgmTkqAAAAADiFRp8J+uabb9S6dWstX75cPXv2rLNt/fr1SklJUUDAz90qNTVVRUVFKisr09atW3XkyBGlpqbWbo+IiFD37t21bt260zgMAAAAAGiYRp8J6tevn/r163fSbSUlJerWrVudsdjYWEnSnj17VFJSIkmKj4+vt8/evXsbG6WOgAAz13hwOMw8btTHa8FsfP9xAq8Fc/G9xwm8Fn5Zo0vQv1NVVaXAwMA6Y0FBQZKk6upqVVZWStJJ9zl06FCTn9dutykqqlWTHw+0BBERIVZHANAMMBcAYB74ZWe0BAUHB9db4KC6ulqSFBoaquDgYEmSy+Wq/fjEPiEhTf9meTxeOZ1Hm/x4f+Zw2HmhQ5LkdFbK7fZYHQMWYS7ACcwF5mIewAkmzwMRESENOhN2RktQXFycSktL64yd+Lxdu3aqqampHevUqVOdfRITE0/ruWtqzPxGAye43R5+DgAwFwBgHmiAM3rBYEpKijZs2CC32107VlBQoC5duigmJkaJiYkKCwvT2rVra7c7nU5t2bJFl1xyyZmMAgAAAAAndUZL0ODBg1VRUaGxY8dqx44dWrZsmRYsWKC77rpL0vH3AmVmZmratGn66KOPtHXrVj344IOKi4tT//79z2QUAAAAADipM3o5XExMjObOnavJkycrIyNDbdu2VU5OjjIyMmr3yc7OVk1NjcaNG6eqqiqlpKQoLy+v3mIJAAAAAHA2nFYJeuaZZ+qNJSUlacmSJad8jMPh0OjRozV69OjTeWoAAAAAaBIWEQcAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIxCCQIAAABgFEoQAAAAAKNQggAAAAAYhRIEAAAAwCiUIAAAAABGoQQBAAAAMAolCAAAAIBRKEEAAAAAjEIJAgAAAGAUShAAAAAAo1CCAAAAABiFEgQAAADAKJQgAAAAAEahBAEAAAAwiiUlyOPxKDc3V2lpaerZs6dGjBih4uJiK6IAAAAAMIwlJWj27NlavHixJk2apCVLlshmsykrK0sul8uKOAAAAAAM4vMS5HK5NG/ePI0aNUrp6elKTEzUjBkztG/fPq1Zs8bXcQAAAAAYxuclaOvWrTpy5IhSU1NrxyIiItS9e3etW7fO13EAAAAAGMbnJaikpESSFB8fX2c8NjZWe/fu9XUcAAAAAIYJ8PUTVlZWSpICAwPrjAcFBenQoUNN+pp2u03R0a1OO5s/stmO/zk+61LVuD3WhoElAhzH/y2jdesQeb0Wh4FlmAvAXADmATAPHO8FDeHzEhQcHCzp+HuDTnwsSdXV1QoJCWnS17TZbHI4GnbALVVkeJDVEWAxu50V78FcAOYCMA+AeaAhfP43dOIyuNLS0jrjpaWliouL83UcAAAAAIbxeQlKTExUWFiY1q5dWzvmdDq1ZcsWXXLJJb6OAwAAAMAwPr8cLjAwUJmZmZo2bZqio6PVoUMHTZ06VXFxcerfv7+v4wAAAAAwjM9LkCRlZ2erpqZG48aNU1VVlVJSUpSXl1dvsQQAAAAAONNsXq+pa0cAAAAAMBFLRwAAAAAwCiUIAAAAgFEoQQAAAACMQgkCAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEoUU4cOCA1REAWOjYsWP66quvdOTIEaujAAD8ACUIfsfpdOrxxx/Xtm3b5Ha7NWzYMF122WW65pprtGvXLqvjAfCBvXv3asSIEfryyy9VXV2tjIwM3XTTTerXr58KCwutjgfAh959912VlJRIkmbPnq1BgwbpiSeeUHV1tcXJ0JxRguB3pkyZoi+++EIBAQH6y1/+og0bNui5557Teeedp+eee87qeAB8YMqUKTp8+LCio6P1/vvva/fu3Vq0aJGuvPJKTZ061ep4AHxk9uzZGjt2rPbs2aONGzcqNzdXvXr10tq1azVt2jSr46EZowTB73zyySd67rnnlJCQoI8//liXXXaZrr32Wj344IP64osvrI4HwAe++OILTZw4UR07dtQnn3yiK664Qr1791ZWVpY2btxodTwAPpKfn69nn31WvXv31gcffKDk5GQ99dRTmjx5slavXm11PDRjlCD4naNHjyo+Pl6S9Le//U3/8R//IUkKCQmR2+22MhoAHzl27Jhat24tSSooKKidBzwejwICAqyMBsCHSktL1atXL0nHfye4/PLLJUnx8fFyOp1WRkMzx/8p4HdOnAGKj4/X3r17dcUVV0iSli5dqoSEBIvTAfCF7t2766233lJsbKzKy8uVnp4ul8ulOXPmKDEx0ep4AHwkLi5ORUVFcrlc2rZtm8aPHy9JWr9+veLi4qwNh2aNEgS/k52drVGjRunYsWMaNGiQOnfurClTpmjhwoX605/+ZHU8AD7w6KOPauTIkSovL1dWVpbi4uI0fvx4ffjhh8rLy7M6HgAfueWWW3T//fcrKChIF154oXr16qWFCxdq6tSpGjVqlNXx0IzZvF6v1+oQQGOVl5dr3759tf/iu3nzZoWFhXEmCDCI1+vV4cOHFRERIUkqKipSZGSkoqKiLE4GwJf+8pe/aNeuXbruuusUFRWl5cuXq7q6WjfddJPV0dCMUYLg9w4cOKC///3v6tGjh84991yr4wDwkaqqKtntdgUGBmrnzp36+OOP1atXL/Xu3dvqaAAsdODAAUVHR1sdA80cCyPA72zfvl3/+Z//qXXr1unw4cO67rrr9MADD2jgwIGsDgcYYt26dbriiiu0YcMG7d+/XzfddJNeeuklZWZm6r333rM6HgAf4d6BaCpKEPzOs88+q/POO0/nn3++3nvvPdXU1OiTTz7R8OHD9cILL1gdD4APTJ8+XVdeeaV+9atfadWqVQoLC9Nnn32msWPH6pVXXrE6HgAf4d6BaCpKEPzOxo0b9eijjyomJkZ//etflZ6ernbt2ul3v/udtm7danU8AD6wZcsW3XPPPbXlp2/fvgoODlbfvn313XffWR0PgI9w70A0FSUIfufEewDcbre++OILXXrppZKkI0eOKDg42OJ0AHwhJCRELpdLLpdL69evr50HfvrpJ4WHh1ucDoCvcO9ANBVLZMPvJCcn6+WXX1abNm1UWVmpK664Qvv27dP06dOVnJxsdTwAPtCnTx9NnTq19oapaWlpKiws1KRJk9SnTx+L0wHwFe4diKbiTBD8zuOPP67CwkItWrRIY8aMUXR0tF599VXt2LFDOTk5VscD4ANPPvmkzjnnHG3btk1Tp05VWFiY/vznPysgIECPPfaY1fEA+Eh2draefvppjRw5ss69A+fPn6/77rvP6nhoxlgiGy1CWVmZIiMj5XA4rI4CwCIul0uBgYFWxwDgY9w7EE1BCYJfqqmpUVlZWe31vl6vVy6XS5s3b9YNN9xgbTgAPnHgwAEVFRXJ4/FIqjsP3HvvvRanA2C1PXv2qH379lbHQDNFCYLfKSgo0OjRo1VWVlZvW3BwsDZu3GhBKgC+tHLlSo0ZM0bV1dWy2Wzyer2y2WySpA4dOujDDz+0OCEAX/jxxx/17LPP1t4nSPr5H0QOHDigLVu2WJwQzRXvCYLfmT59ui6++GLNnTtXwcHBmjVrlsaMGaOwsDBNnTrV6ngAfODll1/WoEGDtHr1aoWHh+vtt9/Wn/70J8XGxmrUqFFWxwPgI5MmTdL27dt1zTXXaN++fRo4cKB69Oihn376SePHj7c6HpoxVoeD39m2bZveeustXXjhherevbtCQ0M1dOhQhYaGKi8vT1dddZXVEQGcZd9//71mzpypzp0766KLLtKBAwfUr18/1dTU6OWXX9b1119vdUQAPrB+/Xq99NJLSklJ0aeffqqrrrpKSUlJmjFjhj755BP9/ve/tzoiminOBMHvOBwOhYWFSZI6d+6s7du3S5JSU1O1c+dOK6MB8JGgoCCdc845ko7PA99++60k6eKLL1ZxcbGV0QD4UHV1tTp27ChJOv/887Vt2zZJ0g033KDNmzdbGQ3NHCUIficxMVFr1qyRJHXp0kUbNmyQJJWUlFgZC4APJSUlafHixZKkrl276vPPP5ck7dixo7YcAWj5zj333Np/DO3cubMKCwslSR6PR0eOHLEyGpo5LoeD38nKytJ9992nwMBADRw4ULm5ubrzzju1bds2paamWh0PgA/ce++9uv322xUdHa0bb7xRs2bN0sCBA7V3714NGDDA6ngAfOTGG29UTk6OnnnmGaWnp2vo0KFq3769Pv/8c1144YVWx0Mzxupw8EvffPONHA6HEhMTtW7dOs2bN0/x8fHKzs5WZGSk1fEA+MC+ffvkcrl07rnnaufOnXrzzTcVHx+voUOHcr8gwCDz589X586d1bdvX82ZM0cvv/yy4uPj9dxzz6l79+5Wx0MzRQkCAAAAYBQuh4NfeOyxxxq875QpU85iEgBWufXWWxu87+uvv34WkwCw0jvvvNPgfbmBOk6FEgS/8OOPP1odAYDFOnToYHUEAM3Af/3XfzVoP5vNRgnCKXE5HFqE6upqBQUFWR0DgI+5XK7a9//s3btX8fHxFicCAPgDlsiG36mqqlJOTo5eeuml2rGrr75a48aNk8vlsjAZAF8pKyvTkCFDNGvWrNqxG264QcOGDdOhQ4csTAbA195++22tWrWq9vP77rtPf/7zny1MBH9ACYLfefrpp7V582alpKTUjo0bN07r1q3TjBkzLEwGwFcmT56smpoaXX/99bVjr732miorK/Xcc89ZmAyAL82fP1+TJk1SZWVl7Vj79u315JNP6q233rIwGZo7LoeD37n88ss1a9YsJScn1xlfv369HnzwQf31r3+1JhgAn+nTp48WLFigxMTEOuNff/21srKyVFBQYFEyAL7Uv39/Pfzww/rtb39bZ3zlypV68cUXtXr1aouSobnjTBD8zpEjRxQeHl5vPCoqSocPH7YgEQBfc7vd8ng89cYDAgJUXV1tQSIAVigtLT3pvYCSkpK0Z88eCxLBX1CC4Hd69eqlV155RW63u3bM6/VqwYIF+tWvfmVhMgC+0qdPHz3//PN1/uGjoqJCubm5dS6VBdCydenSRWvWrKk3/vHHH+vcc8+1IBH8BZfDwe98/fXXGjp0qKKiotSjRw/ZbDZ98803OnjwoObNm6eePXtaHRHAWbZr1y798Y9/VEVFhbp06SJJ+v777xUZGam8vLzaMQAt24oVK5STk6MBAwaoZ8+estls+uqrr7Ry5UpNmjSJJbJxSpQg+KXdu3dryZIl2r59uwICApSQkKAhQ4YoNjbW6mgAfKSiokIrV66snQe6du2qa6+9VsHBwVZHA+BDq1ev1vz587Vt2zadc845SkhI0J133qnf/OY3VkdDM0YJAgAAAGAU3hMEAAAAwCiUIAAAAABGoQQBAAAAMAolCH5nz549Otlb2WpqavTll19akAgAAAD+hIUR4Hcuuugiff7554qOjq4z/v333+v666/X5s2bLUoG4Gx65513Grwvy+ICAP4dShD8wsKFCzVv3jxJx5fHjo+Pl91e90Sm0+lUmzZt9N5771kREcBZlpiYWOdzm80mr9erkJAQBQQE6PDhw3I4HIqKitJnn31mUUoAZ1tiYqJsNluD9i0sLDzLaeCvAqwOADTEjTfeqPLycnm9Xv3pT3/Sb3/7W7Vq1arOPq1atdLVV19tUUIAZ9vWrVtrP161apXmzJmjKVOm1JajoqIiPfbYYxo4cKBVEQH4wNNPP11bgvbs2aNXX31VN998s3r16qVzzjlHX375pRYtWqS7777b4qRozjgTBL8za9Ys3X777QoJCbE6CgCL9OvXTzNmzFDPnj3rjH/11Ve6++67ORMEGGLo0KG6/vrr9bvf/a7O+PLly7VgwQLl5+dblAzNHQsjwO/cd999Ki8vV0VFhSTpiy++0MSJE7VixQqLkwHwlYMHDyooKKjeuMfjUVVVlQWJAFjhyy+/VEpKSr3xpKQk7dixw4JE8BeUIPidNWvW6Oqrr9amTZu0a9cu3XHHHSooKNC4ceO0cOFCq+MB8IE+ffpo4sSJ+vHHH2vHdu7cqQkTJqhv377WBQPgU506dTrpP4IuWbJEXbt2tSAR/AWXw8HvZGRk6IorrtD999+vOXPmKD8/X6tXr9Z7772nWbNmsTACYIB9+/bp9ttv186dOxURESHp+OIoSUlJevXVV9W6dWuLEwLwhTVr1ig7O1u//vWv9atf/Uper1f/+Mc/VFhYqDlz5ig1NdXqiGimKEHwO0lJSXrvvffUoUMHDR06VImJiRo7dqz27Nmj3/72t9wrCDCE2+3W3/72N3377beSji+fn5qa2uBVowC0DBs3btQbb7yh7du3Szo+F4wYMaLeipLAP2N1OPidiIgIHT58WBUVFdq0aZNGjBghSfrhhx8UGRlpbTgAPuNwOJSWlqa0tDSrowCwyFNPPaXbbrtNzz//vNVR4GcoQfA76enpeuKJJxQWFqawsDBddtll+tvf/qbx48fzXgDAEEVFRZo4caI2bNigY8eO1dvOvUEAM7zzzjsaPny41THgh7gcDn6nqqpKL7zwgnbt2qWsrCwlJyfrxRdfVHFxsSZMmFDv/kEAWp7bbrtNe/bs0dChQxUeHl5ve0ZGhgWpAPjaww8/rLZt2+q+++5TWFiY1XHgRyhBAAC/k5SUpAULFqhXr15WRwFgoT/84Q/auHGjbDabYmJi6i2d/9FHH1mUDM0dl8MBAPxOVFQUZ30B6LLLLtNll11mdQz4Ic4EAQD8zty5c7V+/XpNnTr1pJfDAQDw71CC4BeGDh3a4GVvX3/99bOcBoDVhg4dqk2bNsntdismJkaBgYF1tnMJDNCyVVdX69tvv1XXrl0VHByswsJCvfHGG9q3b58uuOACDRs2TO3atbM6JpoxLoeDX7j00kv14osv6vzzz1dSUpLVcQBYrE+fPurTp4/VMQBYYOfOnRo2bJj279+v9u3ba9KkSbrnnnvUsWNHJSQk6MMPP9SyZcu0aNEiJSQkWB0XzRRnguA35s+fr9zcXC1fvlwdO3a0Og4AALDAXXfdpaCgIN1zzz3Ky8vTBx98oOuuu04TJ06UzWZTTU2NcnJy5HQ6NXfuXKvjopmiBMGv3HHHHYqMjNS0adOsjgLAAkVFRVqxYoUOHTqktLQ0paen19leUVGhyZMna8qUKRYlBHC29erVS/n5+Tr//PP1008/6fLLL9eyZcvUvXv32n22b9+um2++WRs3brQwKZozu9UBgMaYPHmyBg4caHUMABbYsGGDMjIytGLFCn366acaOXKkRo0aJZfLVbtPVVWV3nnnHetCAjjrgoODVVVVJUlq06aNfv/739dbGtvpdLJoCv4tShD8Srt27fSb3/zG6hgALPD888/rd7/7nd5//3198MEHmj59uj7//HONHDlSx44dszoeAB+5/PLL9dRTT2nnzp2SpIkTJ9a+98fr9Wrt2rV64okndNVVV1kZE80cJQgA4Be2bdumzMzM2s+vueYazZkzRxs3blROTo6FyQD40mOPPSa3263Zs2fX27Zq1Srddttt6tChgx566CEL0sFf8J4gAIBfSE9P1wsvvKBevXrVGf/www+VnZ2toUOHKisrS2lpaSosLLQoJQBfOXjwoCIjI+uMHThwQKWlpUpMTLQmFPwGZ4IAAH4hPT1dEydO1ObNm+tc/nbVVVdpzJgxWrBggSZOnGhhQgC+9K8FSJKio6MpQGgQShAAwC88/PDDioqK0i233KKCgoI62zIzM/XEE0/oL3/5i0XpAAD+hMvhAAB+5YcfflBUVNRJV34qKirSBx98oLvuusuCZAAAf0EJAgAAAGAULocDAAAAYBRKEAAAAACjUIIAAAAAGIUSBAAAAMAolCAAAAAARqEEAQAAADAKJQgAAACAUShBAAAAAIzy/wE/Yp4cA/U2aAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "6aa2a284",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "88429292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "cfb23b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin  Embarked  Title  \n",
       "0   7.2500   NaN         0      0  \n",
       "1  71.2833   C85         1      2  \n",
       "2   7.9250   NaN         0      1  \n",
       "3  53.1000  C123         0      2  \n",
       "4   8.0500   NaN         0      0  "
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "57d88d14",
   "metadata": {},
   "outputs": [
    {
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       "      <td>D56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17601</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17604</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A./5. 2152</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11668</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.C./A.4. 23567</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0             1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  3.0      1      0          PC 17599   \n",
       "2             3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  2.0      1      0            113803   \n",
       "4             5         0       3    0  2.0      0      0            373450   \n",
       "5             6         0       3    0  2.0      0      0            330877   \n",
       "6             7         0       1    0  3.0      0      0             17463   \n",
       "7             8         0       3    0  0.0      3      1            349909   \n",
       "8             9         1       3    1  2.0      0      2            347742   \n",
       "9            10         1       2    1  0.0      1      0            237736   \n",
       "10           11         1       3    1  0.0      1      1           PP 9549   \n",
       "11           12         1       1    1  3.0      0      0            113783   \n",
       "12           13         0       3    0  1.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  3.0      1      5            347082   \n",
       "14           15         0       3    1  0.0      0      0            350406   \n",
       "15           16         1       2    1  3.0      0      0            248706   \n",
       "16           17         0       3    0  0.0      4      1            382652   \n",
       "17           18         1       2    0  2.0      0      0            244373   \n",
       "18           19         0       3    1  2.0      1      0            345763   \n",
       "19           20         1       3    1  2.0      0      0              2649   \n",
       "20           21         0       2    0  2.0      0      0            239865   \n",
       "21           22         1       2    0  2.0      0      0            248698   \n",
       "22           23         1       3    1  0.0      0      0            330923   \n",
       "23           24         1       1    0  2.0      0      0            113788   \n",
       "24           25         0       3    1  0.0      3      1            349909   \n",
       "25           26         1       3    1  3.0      1      5            347077   \n",
       "26           27         0       3    0  2.0      0      0              2631   \n",
       "27           28         0       1    0  1.0      3      2             19950   \n",
       "28           29         1       3    1  1.0      0      0            330959   \n",
       "29           30         0       3    0  2.0      0      0            349216   \n",
       "30           31         0       1    0  3.0      0      0          PC 17601   \n",
       "31           32         1       1    1  2.0      1      0          PC 17569   \n",
       "32           33         1       3    1  1.0      0      0            335677   \n",
       "33           34         0       2    0  4.0      0      0        C.A. 24579   \n",
       "34           35         0       1    0  2.0      1      0          PC 17604   \n",
       "35           36         0       1    0  3.0      1      0            113789   \n",
       "36           37         1       3    0  2.0      0      0              2677   \n",
       "37           38         0       3    0  1.0      0      0        A./5. 2152   \n",
       "38           39         0       3    1  1.0      2      0            345764   \n",
       "39           40         1       3    1  0.0      1      0              2651   \n",
       "40           41         0       3    1  3.0      1      0              7546   \n",
       "41           42         0       2    1  2.0      1      0             11668   \n",
       "42           43         0       3    0  2.0      0      0            349253   \n",
       "43           44         1       2    1  0.0      1      2     SC/Paris 2123   \n",
       "44           45         1       3    1  1.0      0      0            330958   \n",
       "45           46         0       3    0  2.0      0      0   S.C./A.4. 23567   \n",
       "46           47         0       3    0  2.0      1      0            370371   \n",
       "47           48         1       3    1  1.0      0      0             14311   \n",
       "48           49         0       3    0  2.0      2      0              2662   \n",
       "49           50         0       3    1  1.0      1      0            349237   \n",
       "\n",
       "        Fare        Cabin  Embarked  Title  \n",
       "0     7.2500          NaN         0      0  \n",
       "1    71.2833          C85         1      2  \n",
       "2     7.9250          NaN         0      1  \n",
       "3    53.1000         C123         0      2  \n",
       "4     8.0500          NaN         0      0  \n",
       "5     8.4583          NaN         2      0  \n",
       "6    51.8625          E46         0      0  \n",
       "7    21.0750          NaN         0      3  \n",
       "8    11.1333          NaN         0      2  \n",
       "9    30.0708          NaN         1      2  \n",
       "10   16.7000           G6         0      1  \n",
       "11   26.5500         C103         0      1  \n",
       "12    8.0500          NaN         0      0  \n",
       "13   31.2750          NaN         0      0  \n",
       "14    7.8542          NaN         0      1  \n",
       "15   16.0000          NaN         0      2  \n",
       "16   29.1250          NaN         2      3  \n",
       "17   13.0000          NaN         0      0  \n",
       "18   18.0000          NaN         0      2  \n",
       "19    7.2250          NaN         1      2  \n",
       "20   26.0000          NaN         0      0  \n",
       "21   13.0000          D56         0      0  \n",
       "22    8.0292          NaN         2      1  \n",
       "23   35.5000           A6         0      0  \n",
       "24   21.0750          NaN         0      1  \n",
       "25   31.3875          NaN         0      2  \n",
       "26    7.2250          NaN         1      0  \n",
       "27  263.0000  C23 C25 C27         0      0  \n",
       "28    7.8792          NaN         2      1  \n",
       "29    7.8958          NaN         0      0  \n",
       "30   27.7208          NaN         1      3  \n",
       "31  146.5208          B78         1      2  \n",
       "32    7.7500          NaN         2      1  \n",
       "33   10.5000          NaN         0      0  \n",
       "34   82.1708          NaN         1      0  \n",
       "35   52.0000          NaN         0      0  \n",
       "36    7.2292          NaN         1      0  \n",
       "37    8.0500          NaN         0      0  \n",
       "38   18.0000          NaN         0      1  \n",
       "39   11.2417          NaN         1      1  \n",
       "40    9.4750          NaN         0      2  \n",
       "41   21.0000          NaN         0      2  \n",
       "42    7.8958          NaN         1      0  \n",
       "43   41.5792          NaN         1      1  \n",
       "44    7.8792          NaN         2      1  \n",
       "45    8.0500          NaN         0      0  \n",
       "46   15.5000          NaN         2      0  \n",
       "47    7.7500          NaN         2      1  \n",
       "48   21.6792          NaN         1      0  \n",
       "49   17.8000          NaN         0      2  "
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "train.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "db0822e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "a86141af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "437a5f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 30.0)"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "ccd55724",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 512.3292)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "38afa31c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0\n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1\n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2\n",
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "b74b3c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare Cabin  Embarked  Title  \n",
       "0   0.0   NaN         0      0  \n",
       "1   2.0   C85         1      2  \n",
       "2   0.0   NaN         0      1  \n",
       "3   2.0  C123         0      2  \n",
       "4   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "679a04a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B96 B98        4\n",
       "G6             4\n",
       "C23 C25 C27    4\n",
       "C22 C26        3\n",
       "F33            3\n",
       "              ..\n",
       "E34            1\n",
       "C7             1\n",
       "C54            1\n",
       "E36            1\n",
       "C148           1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "057cb5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "6b03a470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "e3c7f9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "0cbe9ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n",
    "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "95d78382",
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "e846cf46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n",
      "D:\\anaconda\\lib\\site-packages\\seaborn\\axisgrid.py:848: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  func(*plot_args, **plot_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 11.0)"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1277.47x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'FamilySize',shade= True)\n",
    "facet.set(xlim=(0, train['FamilySize'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "f30eca09",
   "metadata": {},
   "outputs": [],
   "source": [
    "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n",
    "for dataset in train_test_data:\n",
    "    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "42f19260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare  Cabin  Embarked  Title  FamilySize  \n",
       "0   0.0    2.0         0      0         0.4  \n",
       "1   2.0    0.8         1      2         0.4  \n",
       "2   0.0    2.0         0      1         0.0  \n",
       "3   2.0    0.8         0      2         0.4  \n",
       "4   0.0    2.0         0      0         0.0  "
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "af3d97d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
    "train = train.drop(features_drop, axis=1)\n",
    "test = test.drop(features_drop, axis=1)\n",
    "train = train.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "4a726c15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = train.drop('Survived', axis=1)\n",
    "target = train['Survived']\n",
    "\n",
    "train_data.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "82fd5bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
       "0       3    0  1.0   0.0    2.0         0      0         0.4\n",
       "1       1    1  3.0   2.0    0.8         1      2         0.4\n",
       "2       3    1  1.0   0.0    2.0         0      1         0.0\n",
       "3       1    1  2.0   2.0    0.8         0      2         0.4\n",
       "4       3    0  2.0   0.0    2.0         0      0         0.0\n",
       "5       3    0  2.0   0.0    2.0         2      0         0.0\n",
       "6       1    0  3.0   2.0    1.6         0      0         0.0\n",
       "7       3    0  0.0   1.0    2.0         0      3         1.6\n",
       "8       3    1  2.0   0.0    2.0         0      2         0.8\n",
       "9       2    1  0.0   2.0    1.8         1      2         0.4"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "3091a052",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Classifier Modules\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "1b3c8fa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Survived    891 non-null    int64  \n",
      " 1   Pclass      891 non-null    int64  \n",
      " 2   Sex         891 non-null    int64  \n",
      " 3   Age         891 non-null    float64\n",
      " 4   Fare        891 non-null    float64\n",
      " 5   Cabin       891 non-null    float64\n",
      " 6   Embarked    891 non-null    int64  \n",
      " 7   Title       891 non-null    int64  \n",
      " 8   FamilySize  891 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 62.8 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "e623cf9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "9f68b3ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82222222 0.78651685 0.82022472 0.83146067 0.85393258 0.83146067\n",
      " 0.84269663 0.80898876 0.83146067 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors = 13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "2d280af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kNN Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "8707aea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76666667 0.83146067 0.7752809  0.7752809  0.87640449 0.75280899\n",
      " 0.83146067 0.82022472 0.74157303 0.79775281]\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "cfa7777c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.69"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decision tree Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "3f0134a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8        0.80898876 0.82022472 0.83146067 0.87640449 0.79775281\n",
      " 0.80898876 0.80898876 0.75280899 0.80898876]\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "808c245e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.15"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "fdb7bbea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85555556 0.73033708 0.75280899 0.75280899 0.70786517 0.80898876\n",
      " 0.76404494 0.80898876 0.86516854 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "1c052b42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.78"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "bedefae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83333333 0.79775281 0.83146067 0.82022472 0.84269663 0.82022472\n",
      " 0.84269663 0.85393258 0.84269663 0.86516854]\n"
     ]
    }
   ],
   "source": [
    "clf = SVC()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "0827e91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "341b974d",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC()\n",
    "clf.fit(train_data, target)\n",
    "\n",
    "test_data = test.drop(\"PassengerId\", axis=1).copy()\n",
    "prediction = clf.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "27c572ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": prediction\n",
    "    })\n",
    "\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86382452",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission.head(20)"
   ]
  }
 ],
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